Multi-Modal Frontiers & Vector Architecture
Continuous-Time AI Inference with SSMs
State space models handle long sequences with linear-time complexity and continuous-time dynamics, making them strong on streaming and time-series workloads. They complement transformers rather than replace them. The value is efficiency on long, ordered data where attention becomes costly.
Points clés
- Use SSMs for long sequences, streaming signals, and time-series where attention is costly.
- Compare against transformers on your actual data, not just paper benchmarks.
- Handle irregular timestamps explicitly; continuous-time models can use them directly.
- Keep models small and quantize for edge and low-latency deployment.
- Validate on out-of-distribution regimes and regime shifts.
- Monitor concept drift and retrain or adapt as the process changes.
- Combine with classical filters or physics models when domain knowledge is strong.
- Log predictions and residuals to catch degradation early.
Questions fréquentes
Are SSMs better than transformers?
For long, ordered, streaming data often yes; transformers still lead on general language.
Can SSMs run on edge devices?
Yes, their efficiency and small size suit embedded and mobile inference.
How much data do they need?
Often less than large models, but coverage of operating regimes still matters.
What is the main risk?
Overconfidence on regime shifts the model never saw during training.
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